Heart signal waveform processing system and method

ABSTRACT

A computer-implemented method, computer program product and computing system for receiving a plurality of specimen waveform records, wherein each specimen waveform record includes a specimen heartbeat waveform and a related clinical heart health diagnosis, and each specimen heartbeat waveform includes a plurality of discrete waveform portions; and associating at least one of the plurality of discrete waveform portions of each specimen heartbeat waveform with the related clinical heart health diagnosis to generate a dataset that defines such associations between discrete waveform portions and clinical heart health diagnoses.

RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional Application No.62/746,282, filed on 16 Oct. 2018, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to waveform analysis and, more particularly, toheartbeat waveform analysis.

BACKGROUND

As is known in the art, a Myocardial Infarction (MI), also known as aheart attack, occurs when blood flow decreases or stops to a part of theheart, causing damage to the heart muscle. The most common symptom ischest pain or discomfort, which may travel into the shoulder, arm, back,neck or jaw. Often this pain occurs in the center or left side of thechest and lasts for more than a few minutes. The discomfort mayoccasionally feel like heartburn. Other symptoms may include shortnessof breath, nausea, feeling faint, a cold sweat or feeling tired.

About 30% of people have atypical symptoms, wherein women more oftenpresent without chest pain and instead have neck pain, arm pain or feeltired. Among those over 75 years old, about 5% have had an MyocardialInfarction with little or no history of symptoms. A MyocardialInfarction may cause heart failure, an irregular heartbeat, cardiogenicshock or cardiac arrest. Unfortunately, if the above-described symptomsare not recognized and treatment is not sought in a timely matter (e.g.,less than 90 minutes), permanent damage to the heart muscle or death mayoccur.

SUMMARY OF DISCLOSURE

Concept 4

In one implementation, a computer-implemented method is executed on acomputing system and includes: receiving a plurality of specimenwaveform records, wherein each specimen waveform record includes aspecimen heartbeat waveform and a related clinical heart healthdiagnosis, and each specimen heartbeat waveform includes a plurality ofdiscrete waveform portions; and associating at least one of theplurality of discrete waveform portions of each specimen heartbeatwaveform with the related clinical heart health diagnosis to generate adataset that defines such associations between discrete waveformportions and clinical heart health diagnoses.

One or more of the following features may be included. Associating atleast one of the plurality of discrete waveform portions of eachspecimen heartbeat waveform with the related clinical heart healthdiagnosis may include utilizing machine learning to associate at leastone of the plurality of discrete waveform portions of each specimenheartbeat waveform with the related clinical heart health diagnosis.Associating at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform with the related clinical hearthealth diagnosis may include identifying at least one of the pluralityof discrete waveform portions of each specimen heartbeat waveform thatis at least partially responsible for the related clinical heart healthdiagnosis. Each related clinical heart health diagnosis may beindicative of a person having a heart attack. At least a portion of theplurality of specimen waveform records may include a specimen heartbeatwaveform generated via a conventional 12-lead electrocardiogram.

In another implementation, a computer program product resides on acomputer readable medium and has a plurality of instructions stored onit. When executed by a processor, the instructions cause the processorto perform operations including: receiving a plurality of specimenwaveform records, wherein each specimen waveform record includes aspecimen heartbeat waveform and a related clinical heart healthdiagnosis, and each specimen heartbeat waveform includes a plurality ofdiscrete waveform portions; and associating at least one of theplurality of discrete waveform portions of each specimen heartbeatwaveform with the related clinical heart health diagnosis to generate adataset that defines such associations between discrete waveformportions and clinical heart health diagnoses.

One or more of the following features may be included. Associating atleast one of the plurality of discrete waveform portions of eachspecimen heartbeat waveform with the related clinical heart healthdiagnosis may include utilizing machine learning to associate at leastone of the plurality of discrete waveform portions of each specimenheartbeat waveform with the related clinical heart health diagnosis.Associating at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform with the related clinical hearthealth diagnosis may include identifying at least one of the pluralityof discrete waveform portions of each specimen heartbeat waveform thatis at least partially responsible for the related clinical heart healthdiagnosis. Each related clinical heart health diagnosis may beindicative of a person having a heart attack. At least a portion of theplurality of specimen waveform records may include a specimen heartbeatwaveform generated via a conventional 12-lead electrocardiogram.

In another implementation, a computing system includes a processor andmemory is configured to perform operations including: receiving aplurality of specimen waveform records, wherein each specimen waveformrecord includes a specimen heartbeat waveform and a related clinicalheart health diagnosis, and each specimen heartbeat waveform includes aplurality of discrete waveform portions; and associating at least one ofthe plurality of discrete waveform portions of each specimen heartbeatwaveform with the related clinical heart health diagnosis to generate adataset that defines such associations between discrete waveformportions and clinical heart health diagnoses.

One or more of the following features may be included. Associating atleast one of the plurality of discrete waveform portions of eachspecimen heartbeat waveform with the related clinical heart healthdiagnosis may include utilizing machine learning to associate at leastone of the plurality of discrete waveform portions of each specimenheartbeat waveform with the related clinical heart health diagnosis.Associating at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform with the related clinical hearthealth diagnosis may include identifying at least one of the pluralityof discrete waveform portions of each specimen heartbeat waveform thatis at least partially responsible for the related clinical heart healthdiagnosis. Each related clinical heart health diagnosis may beindicative of a person having a heart attack. At least a portion of theplurality of specimen waveform records may include a specimen heartbeatwaveform generated via a conventional 12-lead electrocardiogram.

In another implementation, a machine-readable dataset includes: aplurality of specimen waveform records, wherein each specimen waveformrecord includes a specimen heartbeat waveform and a related clinicalheart health diagnosis, and each specimen heartbeat waveform includes aplurality of discrete waveform portions; and at least one associationthat associates at least one of the plurality of discrete waveformportions of each specimen heartbeat waveform with the related clinicalheart health diagnosis.

One or more of the following features may be included. Themachine-readable dataset may be generated via machine learning. Eachrelated clinical heart health diagnosis may be indicative of a personhaving a heart attack. At least a portion of the plurality of specimenwaveform records may include a specimen heartbeat waveform generated viaa conventional 12-lead electrocardiogram.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of a distributed computing networkincluding a computing device that executes a waveform analysis processaccording to an embodiment of the present disclosure;

FIG. 2 is a diagrammatic view of the type of heartbeat waveform that maybe processed by the waveform analysis process of FIG. 1 according to anembodiment of the present disclosure;

FIG. 3 is a flowchart of an implementation of the waveform analysisprocess of FIG. 1 according to an embodiment of the present disclosure;

FIG. 4 is a diagrammatic view of a plurality of specimen waveformrecords and the waveform features derived therefrom by the waveformanalysis process of FIG. 1 according to an embodiment of the presentdisclosure; and

FIG. 5 is a flowchart of another implementation of the waveform analysisprocess of FIG. 1 according to an embodiment of the present disclosure;and

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

System Overview

Referring to FIG. 1, there is shown waveform analysis process 10.Waveform analysis process 10 may be implemented as a server-sideprocess, a client-side process, or a hybrid server-side/client-sideprocess. For example, waveform analysis process 10 may be implemented asa purely server-side process via waveform analysis process 10 s.Alternatively, waveform analysis process 10 may be implemented as apurely client-side process via one or more of waveform analysis process10 c 1, waveform analysis process 10 c 2, and waveform analysis process10 c 3. Alternatively still, waveform analysis process 10 may beimplemented as a hybrid server-side/client-side process via waveformanalysis process 10 s in combination with one or more of waveformanalysis process 10 c 1, waveform analysis process 10 c 2, and waveformanalysis process 10 c 3. Accordingly, waveform analysis process 10 asused in this disclosure may include any combination of waveform analysisprocess 10 s, waveform analysis process 10 c 1, waveform analysisprocess 10 c 2 and waveform analysis process 10 c 3.

Waveform analysis process 10 s may be a server application and mayreside on and may be executed by computing device 12, which may beconnected to network 14 (e.g., the Internet or a local area network).Examples of computing device 12 may include, but are not limited to: apersonal computer, a laptop computer, a personal digital assistant, adata-enabled cellular telephone, a notebook computer, a television withone or more processors embedded therein or coupled thereto, acable/satellite receiver with one or more processors embedded therein orcoupled thereto, a server computer, a series of server computers, a minicomputer, a mainframe computer, or a cloud-based computing platform.

The instruction sets and subroutines of waveform analysis process 10 s,which may be stored on storage device 16 coupled to computing device 12,may be executed by one or more processors (not shown) and one or morememory architectures (not shown) included within computing device 12.Examples of storage device 16 may include but are not limited to: a harddisk drive; a RAID device; a random access memory (RAM); a read-onlymemory (ROM); and all forms of flash memory storage devices.

Network 14 may be connected to one or more secondary networks (e.g.,network 18), examples of which may include but are not limited to: alocal area network; a wide area network; or an intranet, for example.

Examples of waveform analysis processes 10 c 1, 10 c 2, 10 c 3 mayinclude but are not limited to a client application, a web browser, anembedded application within a consumer electronic device, or aspecialized application (e.g., an application running on e.g., theAndroid™ platform or the iOS™ platform). The instruction sets andsubroutines of waveform analysis processes 10 c 1, 10 c 2, 10 c 3, whichmay be stored on storage devices 20, 22, 24 (respectively) coupled toclient electronic devices 26, 28, 30 (respectively), may be executed byone or more processors (not shown) and one or more memory architectures(not shown) incorporated into client electronic devices 26, 28, 30(respectively). Examples of storage devices 20, 22, 24 may include butare not limited to: a hard disk drive; a RAID device; a random accessmemory (RAM); a read-only memory (ROM); and all forms of flash memorystorage devices.

Examples of client electronic devices 26, 28, 30 may include, but arenot limited to: waveform analysis kiosk 26 (e.g., which may be placedwithin pharmaceutical stores, shopping malls, medical care facilities,places of public gathering, etc.), smart handheld device 28 (e.g., smarttelephones, personal digital assistants, etc.), connected device 30(e.g., smart clocks, Amazon assistants, Google assistants, etc.), alaptop computer (not shown), a piece of exercise equipment (e.g., atreadmill or an elliptical; not shown) and a dedicated network device(not shown). Client electronic devices 26, 28, 30 may each execute anoperating system, examples of which may include but are not limited toMicrosoft Windows™, Android™, WebOS™, iOS™, Redhat Linux™, or a customoperating system.

Users of client electronic devices 26, 28, 30 may access waveformanalysis process 10 directly through network 14 or through secondarynetwork 18. Further, waveform analysis process 10 may be connected tonetwork 14 through secondary network 18.

The various client electronic devices (e.g., client electronic devices26, 28, 30) may be directly or indirectly coupled to network 14 (ornetwork 18). For example, smart handheld device 28 and connected device30 are shown wirelessly coupled to network 14 via wireless communicationchannels 32, 34 (respectively) established between smart handheld device28 and connected device 30 (respectively) and cellular network/WAP 36,which is shown directly coupled to network 14. Additionally, waveformanalysis kiosk 26 is shown directly coupled to network 14 via ahardwired network connection.

The WAP portion of cellular network/WAP 36 may be, for example, an IEEE802.11a, 802.11b, 802.11g, 802.11n, Wi-Fi, and/or Bluetooth device thatis capable of establishing wireless communication channels 32, 34between smart handheld device 28, connected device 30 (respectively) andthe WAP portion of cellular network/WAP 36. As is known in the art, IEEE802.11x specifications may use Ethernet protocol and carrier sensemultiple access with collision avoidance (i.e., CSMA/CA) for pathsharing. As is known in the art, Bluetooth is a telecommunicationsindustry specification that allows e.g., mobile phones, computers, andpersonal digital assistants to be interconnected using a short-rangewireless connection.

For example, smart watch 38 (e.g., an Apple Watch™ or a Fitbit™ thatincludes electrodes 40, 42 configured to form electrical connectionswith e.g., two fingers on opposite hands of a user) may be configured tobe wirelessly coupled to smart handheld device 28 via wirelesscommunication channel 44 (e.g., a Bluetooth communication channel or anultrasound communication channel), wherein smart watch 38 may beconfigured to acquire heartbeat waveform 46 from a wearer (e.g., user48) of smart watch 38 via waveform acquisition process 50.

Additionally, touchpad device 52 (e.g., a dedicated device that includeselectrodes 54, 56 configured to form electrical connections with e.g.,two fingers on opposite hands of a user) may be configured to bewirelessly coupled to smart handheld device 28 via wirelesscommunication channel 58 (e.g., a Bluetooth communication channel or anultrasound communication channel), wherein touchpad device 52 may beconfigured to acquire heartbeat waveform 46 from a user (e.g., user 48)of touchpad device 52 via waveform acquisition process 50.

Additionally still, steering wheel 60 (e.g., a vehicle steering wheelthat includes electrodes 62, 64 configured to form electricalconnections with e.g., two fingers on opposite hands of a user) may beconfigured to be wirelessly coupled to smart handheld device 28 viawireless communication channel 66 (e.g., a Bluetooth communicationchannel or an ultrasound communication channel), wherein steering wheel60 may be configured to acquire heartbeat waveform 46 from a user (e.g.,user 48) of steering wheel 60 via waveform acquisition process 50.

Further, smart handheld device 28 may include electrodes 68, 70configured to form electrical connections with e.g., two fingers onopposite hands of a user, thus enabling smart handheld device 28 todirectly acquire heartbeat waveform 46 from a user (e.g., user 48) ofsmart handheld device 28 via waveform acquisition process 50 (i.e.,without the need for smart watch 38, touchpad device 52 or steeringwheel 60)

Waveform analysis kiosk 26 may include electrodes 72, 74 configured toform electrical connections with e.g., two fingers on opposite hands ofa user, thus enabling waveform analysis kiosk 26 to directly acquireheartbeat waveform 76 from a user (e.g., user 48) of waveform analysiskiosk 26 via waveform acquisition process 78.

Connected device 30 may include electrodes 80, 82 configured to formelectrical connections with e.g., two fingers on opposite hands of auser, thus enabling connected device 30 to directly acquire heartbeatwaveform 84 from a user (e.g., user 48) of connected device 30 viawaveform acquisition process 86.

Heartbeat Waveform

Referring also to FIG. 2 and as will be discussed below in greaterdetail, there is shown one example of heartbeat waveform 84 (i.e., avoltage versus time representation of the electrical activity of abeating heart) in normal sinus rhythm. As is known in the art, a sinusrhythm is any cardiac rhythm in which depolarization of the cardiacmuscle begins at the sinus node. It is characterized by the presence ofcorrectly oriented P waves on the electrocardiogram (ECG). Sinus rhythmis necessary, but not sufficient, for normal electrical activity withinthe heart.

Letters (PQRST) are used to indicate important points within one cycleof heartbeat waveform 84. PR Interval portion 102 indicatesatrioventricular conduction time, wherein PR Interval portion 102 ismeasured from where the P wave (i.e., point 104) begins until thebeginning of QRS complex portion 106. QRS complex portion 106 indicatesventricular depolarization, wherein QRS complex portion 106 is measuredfrom the end of PR interval portion 102 to the end of the S wave (i.e.,point 108). QT interval portion 110 indicates ventricular activity(i.e., both depolarization and repolarization), wherein QT intervalportion 110 is measured from the beginning of QRS complex portion 106 tothe end of the T wave (i.e., point 112). ST segment portion 114 tracesthe early part of ventricular repolarization, wherein ST segment portion114 begins at the end of QRS complex portion 106 and continues to thebeginning of the T wave (i.e., point 112). PR segment portion 116 mayindicate certain cardiac disease states (e.g., pericarditis or atrialinfarction), wherein PR segment portion 116 is measured from the end ofthe P wave (i.e., point 104) to the beginning of QRS complex 106.

Waveform Analysis Process

As discussed above, waveform analysis kiosk 26 may be configured toreceive heartbeat waveform 76. Further, smart handheld device 28 may beconfigured to receive heartbeat waveform 46. Additionally, connecteddevice 30 may be configured to receive heartbeat waveform 84.

Further and as discussed above, heartbeat waveforms 46, 76, 84 may beobtained via pairs of electrodes (e.g., electrodes 40 & 42, electrodes54 & 56, electrodes 62 & 64, electrodes 68 & 70, electrodes 72 & 74, andelectrodes 80 & 82). Specifically, heartbeat waveforms 46, 76, 84 may beobtained via a first electrode (of an electrode pair) configured to betouched by a first appendage of the user (e.g., user 48) and a secondelectrode (of the electrode pair) configured to be touched by a secondappendage of the user (e.g., user 48). For example, each of theseelectrode pairs may be configured to form electrical connections withe.g., two fingers on opposite hands of a user (e.g., user 48). Such aconfiguration may result in a differential voltage potential measurementconcerning the heart of the user (e.g., user 48) and, therefore,heartbeat waveforms 46, 76, 84 may be single-lead (i.e., single voltagepotential) heartbeat waveforms. While the pairs of electrodes (e.g.,electrodes 40 & 42, electrodes 54 & 56, electrodes 62 & 64, electrodes68 & 70, electrodes 72 & 74, and electrodes 80 & 82) are described aboveas being incorporated into various electronic devices (e.g., smart watch38, touchpad device 52, steering wheel 60, waveform analysis kiosk 26and connected device 30, respectively), this is for illustrativepurposes only and is not intended to be a limitation of this disclosure,as other configurations are possible and are considered to be within thescope of this disclosure. For example, the “pair of electrodes” may be apair of traditional “peel and stick” electrocardiogram electrodes (notshown) that may be affixed to a user's chest and plugged into a bedsidemonitor (not shown), an ICU monitor (not shown), a multi-parametermonitor (not shown) and automated external defibrillator (not shown).

Referring also to FIG. 3, waveform analysis process 10 may receive 200 asingle-lead heartbeat waveform (e.g., one of heartbeat waveforms 46, 76,84) for a user (e.g., user 48). When receiving 200 the single-leadheartbeat waveform (e.g., one of heartbeat waveforms 46, 76, 84),waveform analysis process 10 may receive 202 the single-lead heartbeatwaveform (e.g., one of heartbeat waveforms 46, 76, 84) from an externaldevice.

For example and as discussed above, smart handheld device 28 may receive202 heartbeat waveform 46 from e.g., smart watch 38, touchpad device 52,or steering wheel 60, wherein smart watch 38, touchpad device 52, andsteering wheel 60 may include a pair of electrodes that enable theacquisition of heartbeat waveform 46 via waveform acquisition process50. Such a waveform acquisition process and an electrode pair may benatively included in such external devices. For example, the currentversion of the Apple Watch™ includes such a waveform acquisition process(e.g., waveform acquisition process 50) and such an electrode pair(e.g., electrodes 40, 42), wherein such smart watches may monitor theheart rhythm of the wearer and e.g., notify the wearer if their heartrate remains above or below a chosen beats per minute (BPM) or if anirregular heart rhythm (e.g., AFib) is detected.

Alternatively, the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84) may be locally acquired. For example andas discussed above, smart handheld device 28 may include electrodes 68,70 configured to form electrical connections with e.g., two fingers onopposite hands of a user, thus enabling smart handheld device 28 todirectly acquire heartbeat waveform 46 from a user (e.g., user 48) ofsmart handheld device 28 via waveform acquisition process 50. Furtherand as discussed above, waveform analysis kiosk 26 may includeelectrodes 72, 74 configured to form electrical connections with e.g.,two fingers on opposite hands of a user, thus enabling waveform analysiskiosk 26 to directly acquire heartbeat waveform 76 from a user (e.g.,user 48) of waveform analysis kiosk 26 via waveform acquisition process78. Additionally and as discussed above, connected device 30 may includeelectrodes 80, 82 configured to form electrical connections with e.g.,two fingers on opposite hands of a user, thus enabling connected device30 to directly acquire heartbeat waveform 84 from a user (e.g., user 48)of connected device 30 via waveform acquisition process 86

Regardless of the manner in which the single-lead heartbeat waveform(e.g., one of heartbeat waveforms 46, 76, 84) is acquired (e.g., from anexternal device or locally acquired), waveform analysis process 10 mayassociate 204 a heart health indicator (e.g., heart health indicator 88)with the single-lead heartbeat waveform (e.g., one of heartbeatwaveforms 46, 76, 84). Examples of such a heart health indicator (e.g.,heart health indicator 88) may include a text-based heart healthindicator or an audio-based heart health indicator. When analyzing thesingle-lead heartbeat waveform (e.g., one of heartbeat waveforms 46, 76,84), the quantity of individual heartbeats analyzed and/or the durationof the heartbeat waveform analyzed may vary depending upon the type ofanalysis being performed. For example, as little as a single heartbeatmay be analyzed or an plurality of heartbeats spanning a longer duration(e.g., thirty seconds) may be analyzed.

This heart health indicator (e.g., heart health indicator 88) may beconfigured to indicate whether the user (e.g., user 46) is having aheart attack. As is known in the art, a heart attack happens when theflow of oxygen-rich blood in one or more of the coronary arteries (whichsupply the heart muscle) suddenly becomes blocked, and a section ofheart muscle can no longer get enough oxygen. This blockage may occurwhen plaque ruptures. If blood flow is not restored quickly (either bymedicine that dissolves the blockage or a catheter placed within theartery that physically opens the blockage), the section of heart musclebegins to die. Accordingly and in the event of the occurrence of a heartattack, the more quickly treatment is administered, the more likely itis that the sufferer will survive the heart attack. Therefore, quicklyrealizing that you are having a heart attack (as opposed to thinkingthat you are experiencing heartburn or indigestion) is of paramountimportance.

For example, heart health indicator 88 may be a binary indicatorconfigured to indicate whether the user (e.g., user 48) is having aheart attack. For example and if the single-lead heartbeat waveform(e.g., one of heartbeat waveforms 46, 76, 84) seems to be normal (i.e.,no heart attack), heart health indicator 88 may be “You do not appear tobe having a heart attack.” However and if the single-lead heartbeatwaveform (e.g., one of heartbeat waveforms 46, 76, 84) seems to beconcerning (i.e., heart attack), heart health indicator 88 may be “Youappear to be having a heart attack. Seek medical attention Immediately”.

Alternatively, heart health indicator 88 need not be binary and may be alikelihood indicator configured to indicate whether the user (e.g., user48) is having a heart attack. For example and if the single-leadheartbeat waveform (e.g., one of heartbeat waveforms 46, 76, 84) seemsto be normal (i.e., no heart attack), heart health indicator 88 may be“There is an 84% probability that you are not having a heart attack.”However and if the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84) seems to be concerning (i.e., heartattack), heart health indicator 88 may be “There is a 90% probabilitythat you are having a heart attack. Seek medical attention Immediately”.

In the event that the device (e.g., waveform analysis kiosk 26, smarthandheld device 28, connected device 30) is capable of externalcommunications, such a device (e.g., waveform analysis kiosk 26, smarthandheld device 28, connected device 30) may offer to (or automatically)contact emergency medical services (e.g., 911 in the United States ofAmerica) in the event that the user (e.g., user 48) is having a heartattack. In the event that the device is capable of autonomous driving(e.g., steering wheel 60 is included within a vehicle having autonomousdriving capabilities) and is capable of external communications,emergency medical services (e.g., 911 in the United States of America)may be contacted and the vehicle may autonomous drive to the closesthospital/trauma center in the event that the user (e.g., user 48) ishaving a heart attack.

Depending upon the manner in which the device (e.g., waveform analysiskiosk 26, smart handheld device 28, connected device 30) is configured,the process of associating 204 a heart health indicator (e.g., hearthealth indicator 88) with the single-lead heartbeat waveform (e.g., oneof heartbeat waveforms 46, 76, 84) may be performed locally or remotely.For example, it is foreseeable that some of the devices (e.g., waveformanalysis kiosk 26 and smart handheld device 28) may have more dataprocessing capabilities; while other devices (e.g., smart watch 38 andconnected device 30) may have less data processing capabilities.

Accordingly and for the devices (e.g., waveform analysis kiosk 26 andsmart handheld device 28) that have more data processing capabilities,the process of associating 204 a heart health indicator (e.g., hearthealth indicator 88) with the single-lead heartbeat waveform (e.g., oneof heartbeat waveforms 46, 76, 84) may be performed locally (as thesedevices have the processing capabilities required to accurately andtimely process the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84).

Conversely and for the devices (e.g., smart watch 38 and connecteddevice 30) that have less data processing capabilities, the process ofassociating 204 a heart health indicator (e.g., heart health indicator88) with the single-lead heartbeat waveform (e.g., one of heartbeatwaveforms 46, 76, 84) may be performed remotely (as these devices do nothave the processing capabilities required to accurately and timelyprocess the single-lead heartbeat waveform (e.g., one of heartbeatwaveforms 46, 76, 84).

Specifically and in such a situation, when associating 204 a hearthealth indicator (e.g., heart health indicator 88) with the single-leadheartbeat waveform (e.g., one of heartbeat waveforms 46, 76, 84),waveform analysis process 10 may provide 206 the single-lead heartbeatwaveform (e.g., one of heartbeat waveforms 46, 76, 84) to an externalcomputing environment (e.g., computing device 12). As discussed above,examples of computing device 12 may include, but are not limited to: apersonal computer, a laptop computer, a personal digital assistant, adata-enabled cellular telephone, a notebook computer, a television withone or more processors embedded therein or coupled thereto, acable/satellite receiver with one or more processors embedded therein orcoupled thereto, a server computer, a series of server computers, a minicomputer, a mainframe computer, or a cloud-based computing platform.

Further and when associating 204 a heart health indicator (e.g., hearthealth indicator 88) with the single-lead heartbeat waveform (e.g., oneof heartbeat waveforms 46, 76, 84), waveform analysis process 10 mayutilize 208 the external computing environment (e.g., computing device12) to associate the heart health indicator (e.g., heart healthindicator 88) with the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84) and may receive 210 the heart healthindicator (e.g., heart health indicator 88) from the external computingenvironment (e.g., computing device 12).

Once a heart health indicator (e.g., heart health indicator 88) isassociated 204 with the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84), waveform analysis process 10 mayprovide 212 the heart health indicator (e.g., heart health indicator 88)to a recipient, wherein examples of such a recipient of the heart healthindicator (e.g., heart health indicator 88) may include but are notlimited to the user (e.g., user 48) or a third party (e.g., a medicalprofessional working at a hospital in which waveform analysis kiosk 26is deployed).

Referring also to FIG. 4, when associating 204 a heart health indicator(e.g., heart health indicator 88) with the single-lead heartbeatwaveform (e.g., one of heartbeat waveforms 46, 76, 84), waveformanalysis process 10 may compare 214 one or more portions (e.g., the PRInterval portion, the QRS complex portion, the QT interval portion, theST segment portion, and the PR segment portion) of the single-leadheartbeat waveform (e.g., one of heartbeat waveforms 46, 76, 84) to oneor more waveform features (e.g., waveform features 300), whereinwaveform features 300 may be essentially waveform rules derived from aplurality of specimen waveform records (e.g., specimen waveform records302).

These waveform features (e.g., waveform features 300) may be generatedvia ML (i.e., Machine Learning) and therefore may be ML-generatedwaveform features. For example, the one or more waveform features (e.g.,waveform features 300) may be generated by processing a plurality ofspecimen waveform records (e.g., specimen waveform records 302), whereineach of the specimen waveform records (e.g., specimen waveform records302) may include a specimen heartbeat waveform (e.g., specimen heartbeatwaveform 304) and a related clinical heart health diagnosis (e.g.,clinical heart health diagnosis 306). At least a portion of theplurality of specimen waveform records (e.g., specimen waveform records302) may include a specimen heartbeat waveform (e.g., specimen heartbeatwaveform 304) that was generated via a conventional 12-leadelectrocardiogram. An example of such a related clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306) may include but isnot limited to whether the patient associated with the particularspecimen waveform record had suffered a heart attack.

For example, specimen waveform records 302 may be a massive data set andmay include millions of waveform records that are processed usingmachine learning/artificial intelligence and probabilistic modelling.Assume that waveform analysis process 10 is configured to processcontent (e.g., specimen waveform records 302), wherein examples ofspecimen waveform records 302 may include but are not limited tounstructured content and structured content.

As is known in the art, structured content may be content that isseparated into independent portions (e.g., fields, columns, features)and, therefore, may have a pre-defined data model and/or is organized ina pre-defined manner. For example, if the structured content concerns anemployee list: a first field, column or feature may define the firstname of the employee; a second field, column or feature may define thelast name of the employee; a third field, column or feature may definethe home address of the employee; and a fourth field, column or featuremay define the hire date of the employee.

Further and as is known in the art, unstructured content may be contentthat is not separated into independent portions (e.g., fields, columns,features) and, therefore, may not have a pre-defined data model and/oris not organized in a pre-defined manner. For example, if theunstructured content concerns the same employee list: the first name ofthe employee, the last name of the employee, the home address of theemployee, and the hire date of the employee may all be combined into onefield, column or feature.

For the following example, assume that specimen waveform records 302 arestructured content, wherein each of the specimen waveform records (e.g.,specimen waveform records 302) includes a specimen heartbeat waveform(e.g., specimen heartbeat waveform 304) and a related clinical hearthealth diagnosis (e.g., clinical heart health diagnosis 306).

When processing content (e.g., specimen waveform records 302), waveformanalysis process 10 may use probabilistic modeling to accomplish suchprocessing, wherein examples of such probabilistic modeling may includebut are not limited to discriminative modeling, generative modeling, orcombinations thereof.

As is known in the art, probabilistic modeling may be used within modernartificial intelligence systems (e.g., waveform analysis process 10), inthat these probabilistic models may provide artificial intelligencesystems with the tools required to autonomously analyze vast quantitiesof data (e.g., specimen waveform records 302).

Examples of the tasks for which probabilistic modeling may be utilizedmay include but are not limited to:

-   -   predicting media (music, movies, books) that a user may like or        enjoy based upon media that the user has liked or enjoyed in the        past;    -   transcribing words spoken by a user into editable text;    -   grouping genes into gene clusters;    -   identifying recurring patterns within vast data sets;    -   filtering email that is believed to be spam from a user's inbox;    -   generating clean (i.e., non-noisy) data from a noisy data set;    -   analyzing (voice-based or text-based) customer feedback; and    -   diagnosing various medical conditions and diseases.

For each of the above-described applications of probabilistic modeling,an initial probabilistic model may be defined, wherein this initialprobabilistic model may be subsequently (e.g., iteratively orcontinuously) modified and revised, thus allowing the probabilisticmodels and the artificial intelligence systems (e.g., waveform analysisprocess 10) to “learn” so that future probabilistic models may be moreprecise and may explain more complex data sets.

Specifically, each specimen heartbeat waveform (e.g., specimen heartbeatwaveform 304) may include a plurality of discrete waveform portions. Forexample and as discussed above, each specimen heartbeat waveform (e.g.,specimen heartbeat waveform 304) may include:

-   -   a PR Interval portion that indicates atrioventricular conduction        time;    -   a QRS complex portion that indicates ventricular depolarization;    -   a QT interval portion that indicates ventricular activity (i.e.,        both depolarization and repolarization);    -   an ST segment portion that traces the early part of ventricular        repolarization; and    -   a PR segment portion that may indicate certain cardiac disease        states (e.g., pericarditis or atrial infarction).

As discussed above, an example of such a related clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306) may include but isnot limited to whether the patient associated with the particularspecimen waveform record had suffered a heart attack.

Accordingly and through use of the above-described machine learning,artificial intelligence and probabilistic modelling, waveform analysisprocess 10 may associate at least one of the plurality of discretewaveform portions (e.g., the PR Interval portion, the QRS complexportion, the QT interval portion, the ST segment portion, and the PRsegment portion) with the related clinical heart health diagnosis (e.g.,whether or not the patient associated with the particular specimenwaveform record had suffered a heart attack).

As discussed above, ST segment portion 114 traces the early part ofventricular repolarization, wherein ST segment portion 114 begins at theend of QRS complex portion 106 and continues to the beginning of the Twave (i.e., point 112). As is known in the art, if ST segment portion114 is elevated, the user may be experiencing a ST-segment elevationmyocardial infarction (STEMI), wherein a STEMI is a serious form ofheart attack in which a coronary artery is completely blocked and alarge part of the heart muscle is unable to receive blood.

Accordingly and when comparing 214 one or more portions (e.g., the PRInterval portion, the QRS complex portion, the QT interval portion, theST segment portion, and the PR segment portion) of the single-leadheartbeat waveform (e.g., one of heartbeat waveforms 46, 76, 84) to oneor more waveform features (e.g., waveform features 300), waveformanalysis process 10 may compare 216 an ST segment portion (e.g., STsegment portion 114) of the single-lead heartbeat waveform (e.g., one ofheartbeat waveforms 46, 76, 84) to one or more ST segment waveformfeatures (e.g., included within waveform features 300) to e.g.,determine if the ST segment portion of the single-lead heartbeatwaveform (e.g., one of heartbeat waveforms 46, 76, 84) is elevated(which may be indicative of user 48 having a heart attack).

As each of the above-described specimen waveform records (e.g., specimenwaveform records 302) includes a specimen heartbeat waveform (e.g.,specimen heartbeat waveform 304) and a related clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306), theabove-described machine learning, artificial intelligence andprobabilistic modelling may be utilized to examine each specimenheartbeat waveform (e.g., specimen heartbeat waveform 304) and itsrelated clinical heart health diagnosis (e.g., clinical heart healthdiagnosis 306) to determine e.g., just how much an ST segment portionneeds to be elevated in order for the user to be considered having aheart attack. Or what particular type/shape/duration of ST segmentportion elevation is indicative of a heart attack. Or if the elevationof any other portions of a specimen heartbeat waveform (alone or whencoupled with an elevation of an ST segment portion of a specimenheartbeat waveform) increases (or decreases) the likelihood that a useris having a heart attack.

Accordingly and since the above-described specimen waveform records(e.g., specimen waveform records 302) includes a specimen heartbeatwaveform (e.g., specimen heartbeat waveform 304) and its relatedclinical heart health diagnosis (e.g., clinical heart health diagnosis306), the above-described machine learning, artificial intelligence andprobabilistic modelling may be utilized to examine each specimenheartbeat waveform (e.g., specimen heartbeat waveform 304) and itsrelated clinical heart health diagnosis (e.g., clinical heart healthdiagnosis 306) to generate waveform features 300, wherein waveformfeatures 300 may be essentially waveform rules derived through theabove-described machine learning, artificial intelligence andprobabilistic modelling.

For example and for illustrative purposes only, waveform analysisprocess 10 may observe that:

-   -   a patient was diagnosed with a heart attack 90% of the time when        Portion X of a specimen heartbeat waveform was elevated by more        than 50%;    -   a patient was diagnosed with a heart attack 95% of the time when        Portion X of a specimen heartbeat waveform was elevated by more        than 50% and Portion Y of a specimen heartbeat waveform was        elevated by more than 30%;    -   a patient was diagnosed with a heart attack 100% of the time        when Portion X of a specimen heartbeat waveform was elevated by        more than 75% and Portion Y of a specimen heartbeat waveform was        elevated by more than 50%; and    -   a patient was diagnosed with a heart attack 0% of the time when        Portion X of a specimen heartbeat waveform was elevated by less        than 5%.

According and through the use of such observations, waveform analysisprocess 10 may derive waveform features 300 (e.g., waveform rules). Thiscombination of waveform features 300 and specimen waveform records 302may form machine-readable dataset 308 (which may be configured to beaccessed/utilized by third parties).

ML-Generated Data Set

Accordingly, machine-readable dataset 308 may include: a plurality ofspecimen waveform records (e.g., specimen waveform records 302), whereineach specimen waveform record includes a specimen heartbeat waveform(e.g., specimen heartbeat waveform 304) and a related clinical hearthealth diagnosis (e.g., clinical heart health diagnosis 306). Eachspecimen heartbeat waveform (e.g., specimen heartbeat waveform 304) mayinclude a plurality of discrete waveform portions (e.g., the PR Intervalportion, the QRS complex portion, the QT interval portion, the STsegment portion, and the PR segment portion). At least one associationmay be made that associates at least one of the plurality of discretewaveform portions (e.g., the PR Interval portion, the QRS complexportion, the QT interval portion, the ST segment portion, and the PRsegment portion) of each specimen heartbeat waveform with the relatedclinical heart health diagnosis. For example, Portion A of a firstpatient's specimen heartbeat waveform was elevated by over 50% and theyhad a heart attack; Portion A of a second patient's specimen heartbeatwaveform was elevated by less than 20% and they did not have a heartattack; Portion B of a third patient's specimen heartbeat waveform waselevated by over 80% and they had a heart attack; and Portion B of afourth patient's specimen heartbeat waveform was elevated by less than40% and they did not have a heart attack.

Generating the Data Set

Referring also to FIG. 5 and when generating machine-readable dataset308, waveform analysis process 10 may receive 400 a plurality ofspecimen waveform records (e.g., specimen waveform records 302), whereineach specimen waveform record includes a specimen heartbeat waveform(e.g., specimen heartbeat waveform 304) and a related clinical hearthealth diagnosis (e.g., clinical heart health diagnosis 306). Asdiscussed above, each specimen heartbeat waveform (e.g., specimenheartbeat waveform 304) may include a plurality of discrete waveformportions (e.g., the PR Interval portion, the QRS complex portion, the QTinterval portion, the ST segment portion, and the PR segment portion).

Waveform analysis process 10 may associate 402 at least one of theplurality of discrete waveform portions (e.g., the PR Interval portion,the QRS complex portion, the QT interval portion, the ST segmentportion, and the PR segment portion) of each specimen heartbeat waveform(e.g., specimen heartbeat waveform 304) with the related clinical hearthealth diagnosis (e.g., clinical heart health diagnosis 306) to generatea dataset (e.g., machine-readable dataset 308) that defines suchassociations between discrete waveform portions (e.g., the PR Intervalportion, the QRS complex portion, the QT interval portion, the STsegment portion, and the PR segment portion) and clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306). As discussedabove, examples of such associations may include but are not limited to:Portion A of a first patient's specimen heartbeat waveform was elevatedby over 50% and they had a heart attack; Portion A of a second patient'sspecimen heartbeat waveform was elevated by less than 20% and they didnot have a heart attack; Portion B of a third patient's specimenheartbeat waveform was elevated by over 80% and they had a heart attack;and Portion B of a fourth patient's specimen heartbeat waveform waselevated by less than 40% and they did not have a heart attack.

As discussed above, when associating 402 at least one of the pluralityof discrete waveform portions (e.g., the PR Interval portion, the QRScomplex portion, the QT interval portion, the ST segment portion, andthe PR segment portion) of each specimen heartbeat waveform (e.g.,specimen heartbeat waveform 304) with the related clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306), waveform analysisprocess 10 may utilize 404 machine learning (e.g., machinelearning/artificial intelligence/probabilistic modelling) to associateat least one of the plurality of discrete waveform portions (e.g., thePR Interval portion, the QRS complex portion, the QT interval portion,the ST segment portion, and the PR segment portion) of each specimenheartbeat waveform (e.g., specimen heartbeat waveform 304) with therelated clinical heart health diagnosis (e.g., clinical heart healthdiagnosis 306).

For example and when associating 402 at least one of the plurality ofdiscrete waveform portions (e.g., the PR Interval portion, the QRScomplex portion, the QT interval portion, the ST segment portion, andthe PR segment portion) of each specimen heartbeat waveform (e.g.,specimen heartbeat waveform 304) with the related clinical heart healthdiagnosis (e.g., clinical heart health diagnosis 306), waveform analysisprocess 10 may identify 406 at least one of the plurality of discretewaveform portions (e.g., the PR Interval portion, the QRS complexportion, the QT interval portion, the ST segment portion, and the PRsegment portion) of each specimen heartbeat waveform (e.g., specimenheartbeat waveform 304) that is at least partially responsible for therelated clinical heart health diagnosis (e.g., clinical heart healthdiagnosis 306). Again, examples of such associations may include but arenot limited to: Portion A of a first patient's specimen heartbeatwaveform was elevated by over 50% and they had a heart attack; Portion Aof a second patient's specimen heartbeat waveform was elevated by lessthan 20% and they did not have a heart attack; Portion B of a thirdpatient's specimen heartbeat waveform was elevated by over 80% and theyhad a heart attack; and Portion B of a fourth patient's specimenheartbeat waveform was elevated by less than 40% and they did not have aheart attack

General

As will be appreciated by one skilled in the art, the present disclosuremay be embodied as a method, a system, or a computer program product.Accordingly, the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present disclosure may take the form of a computer program producton a computer-usable storage medium having computer-usable program codeembodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. More specific examples (a non-exhaustive list) ofthe computer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a transmission media such as those supportingthe Internet or an intranet, or a magnetic storage device. Thecomputer-usable or computer-readable medium may also be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via, for instance, optical scanning of thepaper or other medium, then compiled, interpreted, or otherwiseprocessed in a suitable manner, if necessary, and then stored in acomputer memory. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentdisclosure may be written in an object oriented programming languagesuch as Java, Smalltalk, C++ or the like. However, the computer programcode for carrying out operations of the present disclosure may also bewritten in conventional procedural programming languages, such as the“C” programming language or similar programming languages. The programcode may execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network/a widearea network/the Internet (e.g., network 14).

The present disclosure is described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the disclosure. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, may be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer/special purposecomputer/other programmable data processing apparatus, such that theinstructions, which execute via the processor of the computer or otherprogrammable data processing apparatus, create means for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

These computer program instructions may also be stored in acomputer-readable memory that may direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures may illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustrations, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

A number of implementations have been described. Having thus describedthe disclosure of the present application in detail and by reference toembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of thedisclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method, executed on acomputing system, comprising: receiving a plurality of specimen waveformrecords, wherein each specimen waveform record includes a specimenheartbeat waveform and a related clinical heart health diagnosis, andeach specimen heartbeat waveform includes a plurality of discretewaveform portions; and associating at least one of the plurality ofdiscrete waveform portions of each specimen heartbeat waveform with therelated clinical heart health diagnosis to generate a dataset thatdefines such associations between discrete waveform portions andclinical heart health diagnoses.
 2. The computer-implemented method ofclaim 1 wherein associating at least one of the plurality of discretewaveform portions of each specimen heartbeat waveform with the relatedclinical heart health diagnosis includes: utilizing machine learning toassociate at least one of the plurality of discrete waveform portions ofeach specimen heartbeat waveform with the related clinical heart healthdiagnosis.
 3. The computer-implemented method of claim 1 whereinassociating at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform with the related clinical hearthealth diagnosis includes: identifying at least one of the plurality ofdiscrete waveform portions of each specimen heartbeat waveform that isat least partially responsible for the related clinical heart healthdiagnosis.
 4. The computer-implemented method of claim 1 wherein eachrelated clinical heart health diagnosis is indicative of a person havinga heart attack.
 5. The computer-implemented method of claim 1 wherein atleast a portion of the plurality of specimen waveform records includes aspecimen heartbeat waveform generated via a conventional 12-leadelectrocardiogram.
 6. A computer program product residing on a computerreadable medium having a plurality of instructions stored thereon which,when executed by a processor, cause the processor to perform operationscomprising: receiving a plurality of specimen waveform records, whereineach specimen waveform record includes a specimen heartbeat waveform anda related clinical heart health diagnosis, and each specimen heartbeatwaveform includes a plurality of discrete waveform portions; andassociating at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform with the related clinical hearthealth diagnosis to generate a dataset that defines such associationsbetween discrete waveform portions and clinical heart health diagnoses.7. The computer program product of claim 6 wherein associating at leastone of the plurality of discrete waveform portions of each specimenheartbeat waveform with the related clinical heart health diagnosisincludes: utilizing machine learning to associate at least one of theplurality of discrete waveform portions of each specimen heartbeatwaveform with the related clinical heart health diagnosis.
 8. Thecomputer program product of claim 6 wherein associating at least one ofthe plurality of discrete waveform portions of each specimen heartbeatwaveform with the related clinical heart health diagnosis includes:identifying at least one of the plurality of discrete waveform portionsof each specimen heartbeat waveform that is at least partiallyresponsible for the related clinical heart health diagnosis.
 9. Thecomputer program product of claim 6 wherein each related clinical hearthealth diagnosis is indicative of a person having a heart attack. 10.The computer program product of claim 6 wherein at least a portion ofthe plurality of specimen waveform records includes a specimen heartbeatwaveform generated via a conventional 12-lead electrocardiogram.
 11. Acomputing system including a processor and memory configured to performoperations comprising: receiving a plurality of specimen waveformrecords, wherein each specimen waveform record includes a specimenheartbeat waveform and a related clinical heart health diagnosis, andeach specimen heartbeat waveform includes a plurality of discretewaveform portions; and associating at least one of the plurality ofdiscrete waveform portions of each specimen heartbeat waveform with therelated clinical heart health diagnosis to generate a dataset thatdefines such associations between discrete waveform portions andclinical heart health diagnoses.
 12. The computing system of claim 11wherein associating at least one of the plurality of discrete waveformportions of each specimen heartbeat waveform with the related clinicalheart health diagnosis includes: utilizing machine learning to associateat least one of the plurality of discrete waveform portions of eachspecimen heartbeat waveform with the related clinical heart healthdiagnosis.
 13. The computing system of claim 11 wherein associating atleast one of the plurality of discrete waveform portions of eachspecimen heartbeat waveform with the related clinical heart healthdiagnosis includes: identifying at least one of the plurality ofdiscrete waveform portions of each specimen heartbeat waveform that isat least partially responsible for the related clinical heart healthdiagnosis.
 14. The computing system of claim 11 wherein each relatedclinical heart health diagnosis is indicative of a person having a heartattack.
 15. The computing system of claim 11 wherein at least a portionof the plurality of specimen waveform records includes a specimenheartbeat waveform generated via a conventional 12-leadelectrocardiogram.
 16. A machine-readable dataset comprising: aplurality of specimen waveform records, wherein each specimen waveformrecord includes a specimen heartbeat waveform and a related clinicalheart health diagnosis, and each specimen heartbeat waveform includes aplurality of discrete waveform portions; and at least one associationthat associates at least one of the plurality of discrete waveformportions of each specimen heartbeat waveform with the related clinicalheart health diagnosis.
 17. The machine-readable dataset of claim 16wherein the machine-readable dataset is generated via machine learning.18. The machine-readable dataset of claim 16 wherein each relatedclinical heart health diagnosis is indicative of a person having a heartattack.
 19. The machine-readable dataset of claim 16 wherein at least aportion of the plurality of specimen waveform records includes aspecimen heartbeat waveform generated via a conventional 12-leadelectrocardiogram.